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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: February 2026
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 117
Article Submitted : 20845
Article Published : 8362
Total Authors : 22027
Total Reviewer : 793
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| Paper Title: | Abnormal Activity Detection and Classification of Bus Passengers with In-Vehicle Image Sensing. |
| Authors Name: | ROHINI A , Dr. ARAVINDA T.V , Dr. KRISHNAREDDY K.R , Prof. RAMESH B.E , NAYANA R |
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IJRTI_205530
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| Published Paper Id: | IJRTI2508004 |
| Published In: | Volume 10 Issue 8, August-2025 |
| DOI: | |
| Abstract: | Traditional video surveillance systems in public transportation rely heavily on manual monitoring, which is time-consuming, error-prone, and inefficient in detecting anomalous passenger behavior in real time. This project proposes an intelligent, automated system for detecting passenger actions and identifying anomalies within a bus environment using deep learning techniques. The proposed system integrates YOLOv8, a real-time object detection algorithm, to identify and track passengers from live video streams. Once passengers are detected, the system extracts individual regions of interest and classifies their actions using a fine-tuned VGG16 convolutional neural network. The model is trained to distinguish between normal behaviors (e.g., sitting, standing) and anomalous actions (e.g., falling, squanting), enabling timely alerts for safety interventions. This dual-stage approach—combining spatial detection with semantic classification—ensures accurate and robust identification of potentially hazardous situations. The system supports real-time processing and can be enhanced with alert mechanisms such as visual warnings, sound alarms, or event logging. By automating the detection of unsafe or unusual behavior, the system significantly improves passenger safety, reduces the burden on human operators, and contributes to smarter public transportation surveillance systems. |
| Keywords: | YOLOv8, VGG16, falling, sitting, Video surveillance |
| Cite Article: | "Abnormal Activity Detection and Classification of Bus Passengers with In-Vehicle Image Sensing.", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 8, page no.a20-a28, August-2025, Available :http://www.ijrti.org/papers/IJRTI2508004.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2508004
Registration ID:205530
Published In: Volume 10 Issue 8, August-2025
DOI (Digital Object Identifier):
Page No: a20-a28 Country: chitradurga, karnataka, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2508004 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2508004 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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